Introduction to geopandas and cartopy
Contents
Introduction to geopandas and cartopy#
Basic Setup#
Again we will be using pandas and matplotlib.
import pandas as pd
import matplotlib.pyplot as plt
We’ll also supress a few disturbing warnings.
import warnings
warnings.filterwarnings('ignore')
Why do we need something other than pandas?#
Let’s reload again our example dataset of conventional power plants in Europe as a pd.DataFrame.
fn = "https://raw.githubusercontent.com/PyPSA/powerplantmatching/master/powerplants.csv"
ppl = pd.read_csv(fn, index_col=0)
This dataset includes coordinates (latitude and longitude), which allows us to plot the location and capacity of all power plants in a scatter plot:
ppl.plot.scatter('lon', 'lat', s=ppl.Capacity/1e3)
<AxesSubplot: xlabel='lon', ylabel='lat'>
However, this graphs misses some geographic reference point, we’d normally expect for a map like shorelines, country borders etc.
Geopandas - a Pandas extension for geospatial data#
Geopandas extends pandas by adding support for geospatial data.
The core data structure in GeoPandas is the geopandas.GeoDataFrame, a subclass of pandas.DataFrame, that can store geometry columns and perform spatial operations.
Note
Documentation for this package is available at https://geopandas.org/en/stable/.
Typical geometries are points, lines, and polygons. They come from another library called shapely.
First, we need to import the geopandas package. The conventional alias is gpd:
import geopandas as gpd
We can convert the latitude and longitude values given in the dataset to formal geometries (to be exact: shapely.Point objects but we won’t go into detail regarding this) using the gpd.points_from_xy() function, and use this to gpd.GeoDataFrame. We should also specify a so-called coordinate reference system (CRS). The code ‘4326’ means latitude and longitude values.
geometry = gpd.points_from_xy(ppl['lon'], ppl['lat'])
gdf = gpd.GeoDataFrame(ppl, geometry=geometry, crs=4326)
Now, the gdf looks like this:
gdf.head(3)
| Name | Fueltype | Technology | Set | Country | Capacity | Efficiency | DateIn | DateRetrofit | DateOut | lat | lon | Duration | Volume_Mm3 | DamHeight_m | StorageCapacity_MWh | EIC | projectID | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||
| 0 | Doel | Nuclear | Steam Turbine | PP | Belgium | 2911.0 | NaN | 1975.0 | NaN | 2022.0 | 51.32481 | 4.25889 | NaN | 0.0 | 0.0 | 0.0 | {'22WDOELX3000078D', '22WDOELX2000077N', '22WD... | {'ENTSOE': {'22WDOELX3000078D', '22WDOELX20000... | POINT (4.25889 51.32481) |
| 1 | Sarrans | Hydro | Reservoir | Store | France | 183.0 | NaN | 1932.0 | NaN | NaN | 44.82942 | 2.74042 | NaN | 0.0 | 0.0 | 0.0 | {'17W100P100P02934'} | {'ENTSOE': {'17W100P100P02934'}, 'OPSD': {'OEU... | POINT (2.74042 44.82942) |
| 2 | Pragneres | Hydro | Reservoir | Store | France | 189.2 | NaN | 1953.0 | NaN | NaN | 42.82110 | 0.01033 | NaN | 0.0 | 0.0 | 0.0 | {'17W100P100P02918'} | {'ENTSOE': {'17W100P100P02918'}, 'OPSD': {'OEU... | POINT (0.01033 42.82110) |
With the additional geometry columns, it is now even easier to plot the geographic data:
gdf.plot(
column='Fueltype',
markersize=gdf.Capacity/1e2,
)
<AxesSubplot: >
We can also start up an interactive map to explore the geodata in more detail:
gdf.explore(column='Fueltype')
Make this Notebook Trusted to load map: File -> Trust Notebook
Map Projections with Cartopy#
Cartopy is a Python package designed for geospatial data processing and has exposed an interface to enable easy map creation using matplotlib.
The Earth is a globe, but we present maps usually on two-dimensional surfaces. Hence, we typically need to project data points onto flat surfaces (e.g. screens, paper). However, we will always loose some information in doing so.
A map projection is:
a systematic transformation of the latitudes and longitudes of locations from the surface of a sphere or an ellipsoid into locations on a plane. Wikipedia: Map projection.
Different projections preserve different metric properties. As a result, converting geodata from one projection to another is a common exercise in geographic data science.
conformal projections preserve angles/directions (e.g. Mercator projection)
equal-area projections preserve area measure (e.g. Mollweide)
equidistant projections preserve distances between points (e.g. Plate carrée)
compromise projections seek to strike a balance between distortions (e.g. Robinson)
If you like the “Orange-as-Earth” analogy for projections, checkout this numberphile video by Hannah Fry.
Note
Documentation for this package is available at https://scitools.org.uk/cartopy/docs/latest/.
First, we need to import the relevant parts of the cartopy package:
import cartopy
import cartopy.crs as ccrs
Let’s draw a first map with cartopy outlining the global coastlines in the so-called plate carrée projection (equirectangular projection):
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
<cartopy.mpl.feature_artist.FeatureArtist at 0x7feae46f1850>
Error in callback <function _draw_all_if_interactive at 0x7feb069ceaf0> (for post_execute):
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/pyplot.py:119, in _draw_all_if_interactive()
117 def _draw_all_if_interactive():
118 if matplotlib.is_interactive():
--> 119 draw_all()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/_pylab_helpers.py:132, in Gcf.draw_all(cls, force)
130 for manager in cls.get_all_fig_managers():
131 if force or manager.canvas.figure.stale:
--> 132 manager.canvas.draw_idle()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/backend_bases.py:2054, in FigureCanvasBase.draw_idle(self, *args, **kwargs)
2052 if not self._is_idle_drawing:
2053 with self._idle_draw_cntx():
-> 2054 self.draw(*args, **kwargs)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/backends/backend_agg.py:408, in FigureCanvasAgg.draw(self)
404 # Acquire a lock on the shared font cache.
405 with RendererAgg.lock, \
406 (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
407 else nullcontext()):
--> 408 self.figure.draw(self.renderer)
409 # A GUI class may be need to update a window using this draw, so
410 # don't forget to call the superclass.
411 super().draw()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:74, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
72 @wraps(draw)
73 def draw_wrapper(artist, renderer, *args, **kwargs):
---> 74 result = draw(artist, renderer, *args, **kwargs)
75 if renderer._rasterizing:
76 renderer.stop_rasterizing()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/figure.py:3074, in Figure.draw(self, renderer)
3071 # ValueError can occur when resizing a window.
3073 self.patch.draw(renderer)
-> 3074 mimage._draw_list_compositing_images(
3075 renderer, self, artists, self.suppressComposite)
3077 for sfig in self.subfigs:
3078 sfig.draw(renderer)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/image.py:131, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
129 if not_composite or not has_images:
130 for a in artists:
--> 131 a.draw(renderer)
132 else:
133 # Composite any adjacent images together
134 image_group = []
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:543, in GeoAxes.draw(self, renderer, **kwargs)
535 """
536 Extend the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
537
(...)
540 been set.
541 """
542 # Shared processing steps
--> 543 self._draw_preprocess(renderer)
545 # XXX This interface needs a tidy up:
546 # image drawing on pan/zoom;
547 # caching the resulting image;
548 # buffering the result by 10%...;
549 if not self._done_img_factory:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:509, in GeoAxes._draw_preprocess(self, renderer)
506 # If data has been added (i.e. autoscale hasn't been turned off)
507 # then we should autoscale the view.
508 if self.get_autoscale_on() and self.ignore_existing_data_limits:
--> 509 self.autoscale_view()
511 # Adjust location of background patch so that new gridlines below are
512 # clipped correctly.
513 self.patch._adjust_location()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:946, in GeoAxes.autoscale_view(self, tight, scalex, scaley)
943 matplotlib.axes.Axes.autoscale_view(self, tight=tight,
944 scalex=scalex, scaley=scaley)
945 # Limit the resulting bounds to valid area.
--> 946 if scalex and self._autoscaleXon:
947 bounds = self.get_xbound()
948 self.set_xbound(max(bounds[0], self.projection.x_limits[0]),
949 min(bounds[1], self.projection.x_limits[1]))
AttributeError: 'GeoAxesSubplot' object has no attribute '_autoscaleXon'
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/IPython/core/formatters.py:339, in BaseFormatter.__call__(self, obj)
337 pass
338 else:
--> 339 return printer(obj)
340 # Finally look for special method names
341 method = get_real_method(obj, self.print_method)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/IPython/core/pylabtools.py:151, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
148 from matplotlib.backend_bases import FigureCanvasBase
149 FigureCanvasBase(fig)
--> 151 fig.canvas.print_figure(bytes_io, **kw)
152 data = bytes_io.getvalue()
153 if fmt == 'svg':
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/backend_bases.py:2314, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
2308 renderer = _get_renderer(
2309 self.figure,
2310 functools.partial(
2311 print_method, orientation=orientation)
2312 )
2313 with getattr(renderer, "_draw_disabled", nullcontext)():
-> 2314 self.figure.draw(renderer)
2316 if bbox_inches:
2317 if bbox_inches == "tight":
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:74, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
72 @wraps(draw)
73 def draw_wrapper(artist, renderer, *args, **kwargs):
---> 74 result = draw(artist, renderer, *args, **kwargs)
75 if renderer._rasterizing:
76 renderer.stop_rasterizing()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/figure.py:3074, in Figure.draw(self, renderer)
3071 # ValueError can occur when resizing a window.
3073 self.patch.draw(renderer)
-> 3074 mimage._draw_list_compositing_images(
3075 renderer, self, artists, self.suppressComposite)
3077 for sfig in self.subfigs:
3078 sfig.draw(renderer)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/image.py:131, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
129 if not_composite or not has_images:
130 for a in artists:
--> 131 a.draw(renderer)
132 else:
133 # Composite any adjacent images together
134 image_group = []
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:543, in GeoAxes.draw(self, renderer, **kwargs)
535 """
536 Extend the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
537
(...)
540 been set.
541 """
542 # Shared processing steps
--> 543 self._draw_preprocess(renderer)
545 # XXX This interface needs a tidy up:
546 # image drawing on pan/zoom;
547 # caching the resulting image;
548 # buffering the result by 10%...;
549 if not self._done_img_factory:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:509, in GeoAxes._draw_preprocess(self, renderer)
506 # If data has been added (i.e. autoscale hasn't been turned off)
507 # then we should autoscale the view.
508 if self.get_autoscale_on() and self.ignore_existing_data_limits:
--> 509 self.autoscale_view()
511 # Adjust location of background patch so that new gridlines below are
512 # clipped correctly.
513 self.patch._adjust_location()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:946, in GeoAxes.autoscale_view(self, tight, scalex, scaley)
943 matplotlib.axes.Axes.autoscale_view(self, tight=tight,
944 scalex=scalex, scaley=scaley)
945 # Limit the resulting bounds to valid area.
--> 946 if scalex and self._autoscaleXon:
947 bounds = self.get_xbound()
948 self.set_xbound(max(bounds[0], self.projection.x_limits[0]),
949 min(bounds[1], self.projection.x_limits[1]))
AttributeError: 'GeoAxesSubplot' object has no attribute '_autoscaleXon'
<Figure size 640x480 with 1 Axes>
A list of the available projections can be found on the Cartopy projection list page.
ax = plt.axes(projection=ccrs.Mollweide())
ax.stock_img()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In [12], line 2
1 ax = plt.axes(projection=ccrs.Mollweide())
----> 2 ax.stock_img()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1064, in GeoAxes.stock_img(self, name)
1059 source_proj = ccrs.PlateCarree()
1060 fname = os.path.join(config["repo_data_dir"],
1061 'raster', 'natural_earth',
1062 '50-natural-earth-1-downsampled.png')
-> 1064 return self.imshow(imread(fname), origin='upper',
1065 transform=source_proj,
1066 extent=[-180, 180, -90, 90])
1067 else:
1068 raise ValueError('Unknown stock image %r.' % name)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:318, in _add_transform.<locals>.wrapper(self, *args, **kwargs)
313 raise ValueError(f'Invalid transform: Spherical {func.__name__} '
314 'is not supported - consider using '
315 'PlateCarree/RotatedPole.')
317 kwargs['transform'] = transform
--> 318 return func(self, *args, **kwargs)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1368, in GeoAxes.imshow(self, img, *args, **kwargs)
1364 if not isinstance(transform, ccrs.Projection):
1365 raise ValueError('Expected a projection subclass. Cannot '
1366 'handle a %s in imshow.' % type(transform))
-> 1368 target_extent = self.get_extent(self.projection)
1369 regrid_shape = kwargs.pop('regrid_shape', 750)
1370 regrid_shape = self._regrid_shape_aspect(regrid_shape,
1371 target_extent)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:814, in GeoAxes.get_extent(self, crs)
805 def get_extent(self, crs=None):
806 """
807 Get the extent (x0, x1, y0, y1) of the map in the given coordinate
808 system.
(...)
812
813 """
--> 814 p = self._get_extent_geom(crs)
815 r = p.bounds
816 x1, y1, x2, y2 = r
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:821, in GeoAxes._get_extent_geom(self, crs)
819 def _get_extent_geom(self, crs=None):
820 # Perform the calculations for get_extent(), which just repackages it.
--> 821 with self.hold_limits():
822 if self.get_autoscale_on():
823 self.autoscale_view()
File ~/micromamba-root/envs/esm/lib/python3.9/contextlib.py:117, in _GeneratorContextManager.__enter__(self)
115 del self.args, self.kwds, self.func
116 try:
--> 117 return next(self.gen)
118 except StopIteration:
119 raise RuntimeError("generator didn't yield") from None
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:491, in GeoAxes.hold_limits(self, hold)
488 data_lim = self.dataLim.frozen().get_points()
489 view_lim = self.viewLim.frozen().get_points()
490 other = (self.ignore_existing_data_limits,
--> 491 self._autoscaleXon, self._autoscaleYon)
492 try:
493 yield
AttributeError: 'GeoAxesSubplot' object has no attribute '_autoscaleXon'
Error in callback <function _draw_all_if_interactive at 0x7feb069ceaf0> (for post_execute):
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/pyplot.py:119, in _draw_all_if_interactive()
117 def _draw_all_if_interactive():
118 if matplotlib.is_interactive():
--> 119 draw_all()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/_pylab_helpers.py:132, in Gcf.draw_all(cls, force)
130 for manager in cls.get_all_fig_managers():
131 if force or manager.canvas.figure.stale:
--> 132 manager.canvas.draw_idle()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/backend_bases.py:2054, in FigureCanvasBase.draw_idle(self, *args, **kwargs)
2052 if not self._is_idle_drawing:
2053 with self._idle_draw_cntx():
-> 2054 self.draw(*args, **kwargs)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/backends/backend_agg.py:408, in FigureCanvasAgg.draw(self)
404 # Acquire a lock on the shared font cache.
405 with RendererAgg.lock, \
406 (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
407 else nullcontext()):
--> 408 self.figure.draw(self.renderer)
409 # A GUI class may be need to update a window using this draw, so
410 # don't forget to call the superclass.
411 super().draw()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:74, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
72 @wraps(draw)
73 def draw_wrapper(artist, renderer, *args, **kwargs):
---> 74 result = draw(artist, renderer, *args, **kwargs)
75 if renderer._rasterizing:
76 renderer.stop_rasterizing()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/figure.py:3074, in Figure.draw(self, renderer)
3071 # ValueError can occur when resizing a window.
3073 self.patch.draw(renderer)
-> 3074 mimage._draw_list_compositing_images(
3075 renderer, self, artists, self.suppressComposite)
3077 for sfig in self.subfigs:
3078 sfig.draw(renderer)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/image.py:131, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
129 if not_composite or not has_images:
130 for a in artists:
--> 131 a.draw(renderer)
132 else:
133 # Composite any adjacent images together
134 image_group = []
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:543, in GeoAxes.draw(self, renderer, **kwargs)
535 """
536 Extend the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
537
(...)
540 been set.
541 """
542 # Shared processing steps
--> 543 self._draw_preprocess(renderer)
545 # XXX This interface needs a tidy up:
546 # image drawing on pan/zoom;
547 # caching the resulting image;
548 # buffering the result by 10%...;
549 if not self._done_img_factory:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:509, in GeoAxes._draw_preprocess(self, renderer)
506 # If data has been added (i.e. autoscale hasn't been turned off)
507 # then we should autoscale the view.
508 if self.get_autoscale_on() and self.ignore_existing_data_limits:
--> 509 self.autoscale_view()
511 # Adjust location of background patch so that new gridlines below are
512 # clipped correctly.
513 self.patch._adjust_location()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:946, in GeoAxes.autoscale_view(self, tight, scalex, scaley)
943 matplotlib.axes.Axes.autoscale_view(self, tight=tight,
944 scalex=scalex, scaley=scaley)
945 # Limit the resulting bounds to valid area.
--> 946 if scalex and self._autoscaleXon:
947 bounds = self.get_xbound()
948 self.set_xbound(max(bounds[0], self.projection.x_limits[0]),
949 min(bounds[1], self.projection.x_limits[1]))
AttributeError: 'GeoAxesSubplot' object has no attribute '_autoscaleXon'
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/IPython/core/formatters.py:339, in BaseFormatter.__call__(self, obj)
337 pass
338 else:
--> 339 return printer(obj)
340 # Finally look for special method names
341 method = get_real_method(obj, self.print_method)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/IPython/core/pylabtools.py:151, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
148 from matplotlib.backend_bases import FigureCanvasBase
149 FigureCanvasBase(fig)
--> 151 fig.canvas.print_figure(bytes_io, **kw)
152 data = bytes_io.getvalue()
153 if fmt == 'svg':
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/backend_bases.py:2314, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
2308 renderer = _get_renderer(
2309 self.figure,
2310 functools.partial(
2311 print_method, orientation=orientation)
2312 )
2313 with getattr(renderer, "_draw_disabled", nullcontext)():
-> 2314 self.figure.draw(renderer)
2316 if bbox_inches:
2317 if bbox_inches == "tight":
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:74, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
72 @wraps(draw)
73 def draw_wrapper(artist, renderer, *args, **kwargs):
---> 74 result = draw(artist, renderer, *args, **kwargs)
75 if renderer._rasterizing:
76 renderer.stop_rasterizing()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/figure.py:3074, in Figure.draw(self, renderer)
3071 # ValueError can occur when resizing a window.
3073 self.patch.draw(renderer)
-> 3074 mimage._draw_list_compositing_images(
3075 renderer, self, artists, self.suppressComposite)
3077 for sfig in self.subfigs:
3078 sfig.draw(renderer)
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/image.py:131, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
129 if not_composite or not has_images:
130 for a in artists:
--> 131 a.draw(renderer)
132 else:
133 # Composite any adjacent images together
134 image_group = []
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/matplotlib/artist.py:51, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
48 if artist.get_agg_filter() is not None:
49 renderer.start_filter()
---> 51 return draw(artist, renderer)
52 finally:
53 if artist.get_agg_filter() is not None:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:543, in GeoAxes.draw(self, renderer, **kwargs)
535 """
536 Extend the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
537
(...)
540 been set.
541 """
542 # Shared processing steps
--> 543 self._draw_preprocess(renderer)
545 # XXX This interface needs a tidy up:
546 # image drawing on pan/zoom;
547 # caching the resulting image;
548 # buffering the result by 10%...;
549 if not self._done_img_factory:
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:509, in GeoAxes._draw_preprocess(self, renderer)
506 # If data has been added (i.e. autoscale hasn't been turned off)
507 # then we should autoscale the view.
508 if self.get_autoscale_on() and self.ignore_existing_data_limits:
--> 509 self.autoscale_view()
511 # Adjust location of background patch so that new gridlines below are
512 # clipped correctly.
513 self.patch._adjust_location()
File ~/micromamba-root/envs/esm/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:946, in GeoAxes.autoscale_view(self, tight, scalex, scaley)
943 matplotlib.axes.Axes.autoscale_view(self, tight=tight,
944 scalex=scalex, scaley=scaley)
945 # Limit the resulting bounds to valid area.
--> 946 if scalex and self._autoscaleXon:
947 bounds = self.get_xbound()
948 self.set_xbound(max(bounds[0], self.projection.x_limits[0]),
949 min(bounds[1], self.projection.x_limits[1]))
AttributeError: 'GeoAxesSubplot' object has no attribute '_autoscaleXon'
<Figure size 640x480 with 1 Axes>
We can combine the functionality of cartopy with geopandas plots:
fig = plt.figure(figsize=(7,7))
ax = plt.axes(projection=ccrs.PlateCarree())
gdf.plot(
ax=ax,
column='Fueltype',
markersize=gdf.Capacity/1e2,
)
<GeoAxesSubplot:>
We can add further geographic features to this map for better orientation.
For instance, we can add the coastlines…
ax.coastlines()
fig
… country borders …
ax.add_feature(cartopy.feature.BORDERS, color='grey', linewidth=0.5)
fig
… colour in the ocean in blue …
ax.add_feature(cartopy.feature.OCEAN, color='azure')
fig
…and color in the land area in yellow …
ax.add_feature(cartopy.feature.LAND, color='cornsilk')
fig
Geopandas will automatically calculate sensible bounds for the plot given the geographic data.
But we can also manually zoom in or out by setting the spatial extent with the .set_extent() method:
ax.set_extent([5, 16, 47, 55])
fig
Reprojecting a GeoDataFrame#
In geopandas, we can use the function .to_crs() to convert a GeoDataFrame to a desired coordinate reference system. In this particular case, we use the proj4_init string of an initialised cartopy projection to reproject our power plant GeoDataFrame.
fig = plt.figure(figsize=(7,7))
crs = ccrs.AlbersEqualArea()
ax = plt.axes(projection=crs)
gdf.to_crs(crs.proj4_init).plot(
ax=ax,
column='Fueltype',
markersize=gdf.Capacity/1e2,
)
ax.coastlines()
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbd9d28a070>
Reading and Writing Files with geopandas#
In the following example, we’ll load a dataset containing the NUTS regions:
Nomenclature of Territorial Units for Statistics or NUTS (French: Nomenclature des unités territoriales statistiques) is a geocode standard for referencing the subdivisions of countries for statistical purposes.
Our ultimate goal for this part of the tutorial is to map the power plant capacities to the NUTS-1 region they belong to.
Common filetypes for vector-based geospatial datasets are GeoPackage (.gpkg), GeoJSON (.geojson), File Geodatabase (.gdb), or Shapefiles (.shp).
In geopandas we can use the gpd.read_file() function to read such files. So let’s start:
nuts = gpd.read_file("../../data/nuts/NUTS_RG_10M_2021_4326.geojson")
nuts.head(3)
| id | NUTS_ID | LEVL_CODE | CNTR_CODE | NAME_LATN | NUTS_NAME | MOUNT_TYPE | URBN_TYPE | COAST_TYPE | FID | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BG423 | BG423 | 3 | BG | Pazardzhik | Пазарджик | 3.0 | 2 | 3 | BG423 | POLYGON ((24.42101 42.55306, 24.41032 42.46950... |
| 1 | BG424 | BG424 | 3 | BG | Smolyan | Смолян | 3.0 | 3 | 3 | BG424 | POLYGON ((25.07422 41.79348, 25.05851 41.75177... |
| 2 | BG425 | BG425 | 3 | BG | Kardzhali | Кърджали | 3.0 | 3 | 3 | BG425 | POLYGON ((25.94863 41.32034, 25.90644 41.30757... |
It is good practice to set an index. You can use .set_index() for that:
nuts = nuts.set_index('id')
We can also check out the geometries in the dataset with .geometry:
nuts.geometry
id
BG423 POLYGON ((24.42101 42.55306, 24.41032 42.46950...
BG424 POLYGON ((25.07422 41.79348, 25.05851 41.75177...
BG425 POLYGON ((25.94863 41.32034, 25.90644 41.30757...
CH011 MULTIPOLYGON (((6.86623 46.90929, 6.89621 46.9...
CH012 POLYGON ((8.47767 46.52760, 8.39953 46.48872, ...
...
LV POLYGON ((27.35158 57.51824, 27.54521 57.53444...
ME POLYGON ((20.06394 43.00682, 20.32958 42.91149...
MK POLYGON ((22.36021 42.31116, 22.51041 42.15516...
SK0 POLYGON ((19.88393 49.20418, 19.96275 49.23031...
IT MULTIPOLYGON (((12.47792 46.67984, 12.69064 46...
Name: geometry, Length: 2010, dtype: geometry
With .crs we can check in which coordinate reference system the data is given:
nuts.crs
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
nuts.total_bounds
array([-63.08825, -21.38917, 55.83616, 80.76427])
Let’s filter by NUTS-1 level…
nuts1 = nuts.query("LEVL_CODE == 1")
… and explore what kind of geometries we have in the dataset …
nuts1.explore()
Make this Notebook Trusted to load map: File -> Trust Notebook
To write a GeoDataFrame back to file use GeoDataFrame.to_file(). The file format is inferred from the file ending.
nuts1.to_file("NUTS1.geojson")
Calculating the areas and buffers#
The first thing we need to do to calculate area or buffers is to reproject the GeoDataFrame to an equal-area projection (here: EPSG:3035):
nuts1 = nuts1.to_crs(3035)
The area can be accessed via .area and is given in m² (after projection). Let’s convert to km²:
area = nuts1.area / 1e6
area
id
AT1 23545.286205
AT2 25894.953057
EL4 17388.679384
EE0 45315.713593
EL3 3799.676547
...
PL7 29846.398582
PL8 63217.536546
PL9 35563.812826
RO2 72545.290405
SK0 49008.115415
Length: 125, dtype: float64
nuts1.explore(column=area, vmax=1e5)
Make this Notebook Trusted to load map: File -> Trust Notebook
We can also build a buffer of 1km around each geometry using .buffer():
nuts1.buffer(1000).explore()
Make this Notebook Trusted to load map: File -> Trust Notebook
Joining spatial datasets#
Multiple GeoDataFrames can be combined via spatial joins.
Observations from two datasets are combined with the .sjoin() function based on their spatial relationship to one another (e.g. whether they are intersecting or overlapping). You can read more about the specific options here.
Let’s first reproject the gdf object to the same CRS as nuts1:
gdf = gdf.to_crs(3035)
Then, let’s have a look at both datasets at once. We want to find out which points (representing power plants) lie within which shape (representing NUTS regions).
fig = plt.figure(figsize=(7,7))
ax = plt.axes(projection=ccrs.epsg(3035))
nuts1.plot(
ax=ax,
edgecolor='black',
facecolor='lightgrey'
)
gdf.to_crs(3035).plot(
ax=ax,
column='Fueltype',
markersize=gdf.Capacity/20,
legend=True
)
ax.set_extent([5, 19, 47, 55])
We can now apply the .sjoin function to look for which power plants lie within which NUTS1 region. By default, sjoin looks for intersections and keeps the geometries of the left GeoDataFrame.
joined = gdf.sjoin(nuts1)
If we look at this new GeoDataFrame, we now have additional columns from the NUTS1 data:
joined.head(3)
| Name | Fueltype | Technology | Set | Country | Capacity | Efficiency | DateIn | DateRetrofit | DateOut | ... | index_right | NUTS_ID | LEVL_CODE | CNTR_CODE | NAME_LATN | NUTS_NAME | MOUNT_TYPE | URBN_TYPE | COAST_TYPE | FID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||||
| 0 | Doel | Nuclear | Steam Turbine | PP | Belgium | 2911.0 | NaN | 1975.0 | NaN | 2022.0 | ... | BE2 | BE2 | 1 | BE | Vlaams Gewest | Vlaams Gewest | 0.0 | 0 | 0 | BE2 |
| 170 | Drogenbos Tgv | Natural Gas | CCGT | PP | Belgium | 465.0 | NaN | NaN | NaN | NaN | ... | BE2 | BE2 | 1 | BE | Vlaams Gewest | Vlaams Gewest | 0.0 | 0 | 0 | BE2 |
| 172 | Rodenhuize | Bioenergy | Steam Turbine | PP | Belgium | 268.0 | NaN | NaN | NaN | NaN | ... | BE2 | BE2 | 1 | BE | Vlaams Gewest | Vlaams Gewest | 0.0 | 0 | 0 | BE2 |
3 rows × 29 columns
We can now use these new columns to group the capacities (and convert to a suitable unit):
cap = joined.groupby("NUTS_ID").Capacity.sum() / 1000 # GW
Let’s quickly check if all NUTS1 regions have power plants:
nuts1.index.difference(cap.index)
Index(['CY0', 'ES7', 'FI2', 'FRY', 'IS0', 'LI0', 'MK0', 'MT0', 'PT2', 'PT3',
'TR1', 'TR2', 'TR3', 'TR4', 'TR5', 'TR6', 'TR7', 'TR8', 'TR9', 'TRA',
'TRB', 'TRC'],
dtype='object')
This is not the case. Then it is good practice to reindex the series to include all NUTS1 regions, even if this leads to some NaN values.
cap = cap.reindex(nuts1.index)
cap
id
AT1 4.382100
AT2 4.089500
EL4 0.427967
EE0 1.369000
EL3 0.022142
...
PL7 6.379902
PL8 0.685651
PL9 5.230788
RO2 2.036566
SK0 5.567220
Name: Capacity, Length: 125, dtype: float64
Finally, we can plot the total generation capacity per NUTS1 region on a map.
nuts1.plot(figsize=(7,7), column=cap, legend=True)
<AxesSubplot:>
This concludes the geopandas and cartopy tutorial.